Exploratory analysis of traditional risk factors of ischemic heart disease (IHD) among predominantly Malay Malaysian women

Trends of ischemic heart disease mortality attributable to smoking in the five countries with the highest number of smokers during 1990-2019: an age-period-cohort analysis

Introduction

Smoking increases the risk of various cardiovascular diseases, including ischemic heart disease (IHD). This study aimed to assess the impact of age, period, and cohort on long-term trends in IHD mortality in China, India, Indonesia, the United States, and Russia, the five countries with the highest number of smokers, from 1990 to 2019.

Material and methods

The data were obtained from the Global Burden of Disease (GBD) Study 2019, and the age-standardized mortality rate (ASMR) was calculated. Joinpoint regression analysis was used to assess the magnitude and direction of trends in smoking-attributable mortality from IHD. Age-period-cohort (APC) studies were used to estimate net drift (estimated annual percentage change (EAPC)s), local drift (age-specific EAPCs), and independent trends in age, period, and cohort effects.

Results

The analysis revealed a significant downward trend in ASMRs attributable to IHD as a result of smoking in the United States, India, and Russia. Indonesia and China showed an upward trend. Age effects were increasing for both country and sex, with China showing the most significant increase in the older age group; period effects were decreasing in all countries except Indonesia, and cohort effects were increasing only in Indonesia and China.

Conclusions

From 1990 to 2019, mortality from IHD caused by smoking showed a downward trend in these five countries. However, the pattern of increased mortality from IHD in women caused by smoking warrants further study.

Health effects associated with exposure to secondhand smoke: a Burden of Proof study

Despite a gradual decline in smoking rates over time, exposure to secondhand smoke (SHS) continues to cause harm to nonsmokers, who are disproportionately children and women living in low- and middle-income countries. We comprehensively reviewed the literature published by July 2022 concerning the adverse impacts of SHS exposure on nine health outcomes. Following, we quantified each exposure-response association accounting for various sources of uncertainty and evaluated the strength of the evidence supporting our analyses using the Burden of Proof Risk Function methodology. We found all nine health outcomes to be associated with SHS exposure. We conservatively estimated that SHS increases the risk of ischemic heart disease, stroke, type 2 diabetes and lung cancer by at least around 8%, 5%, 1% and 1%, respectively, with the evidence supporting these harmful associations rated as weak (two stars). The evidence supporting the harmful associations between SHS and otitis media, asthma, lower respiratory infections, breast cancer and chronic obstructive pulmonary disease was weaker (one star). Despite the weak underlying evidence for these associations, our results reinforce the harmful effects of SHS on health and the need to prioritize advancing efforts to reduce active and passive smoking through a combination of public health policies and education initiatives.

Leveraging Mann-Whitney U test on large-scale genetic variation data for analysing malaria genetic markers

Background

The malaria risk analysis of multiple populations is crucial and of great importance whilst compressing limitations. However, the exponential growth in diversity and accumulation of genetic variation data obtained from malaria-infected patients through Genome-Wide Association Studies opens up unprecedented opportunities to explore the significant differences between genetic markers (risk factors), particularly in the resistance or susceptibility of populations to malaria risk. Thus, this study proposes using statistical tests to analyse large-scale genetic variation data, comprising 20,854 samples from 11 populations within three continents: Africa, Oceania, and Asia.

Methods

Even though statistical tests have been utilized to conduct case–control studies since the 1950s to link risk factors to a particular disease, several challenges faced, including the choice of data (ordinal vs. non-ordinal) and test (parametric vs. non-parametric). This study overcomes these challenges by adopting the Mann–Whitney U test to analyse large-scale genetic variation data; to explore the statistical significance of markers between populations; and to further identify the highly differentiated markers.

Results

The findings of this study revealed a significant difference in the genetic markers between populations (p < 0.01) in all the case groups and most control groups. However, for the highly differentiated genetic markers, a significant difference (p < 0.01) was present for most genetic markers with varying p-values between the populations in the case and control groups. Moreover, several genetic markers were observed to have very significant differences (p < 0.001) across all populations, while others exist between certain specific populations. Also, several genetic markers have no significant differences between populations.

Conclusions

These findings further support that the genetic markers contribute differently between populations towards malaria resistance or susceptibility, thus showing differences in the likelihood of malaria infection. In addition, this study demonstrated the robustness of the Mann–Whitney U test in analysing genetic markers in large-scale genetic variation data, thereby indicating an alternative method to explore genetic markers in other complex diseases. The findings hold great promise for genetic markers analysis, and the pipeline emphasized in this study can fully be reproduced to analyse new data.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12936-022-04104-x.

In-Hospital Outcomes of Female Patients With Inferior Wall Myocardial Infarction

Background The aim of this study was to determine the in-hospital outcome of female patients with inferior wall myocardial infarction (MI). Methodology This study was conducted from January to December 2017 at the Department of Cardiology, National Institute of Cardiovascular Disease, Karachi. A total of 59 women admitted with inferior wall MI were enrolled in the study. In all patients, in-hospital outcomes were observed. Descriptive statistics were applied. Stratification was done using chi-square test, and p-value of ≤0.05 was considered significant. Results The mean age of study participants was 58.80 ± 9.17 years, while 247 (79.7%) participants were above 50 years of age. The mean onset of duration of sign and symptoms of inferior wall MI was 3.48 ± 1.53 hours. There were 36 (61.0%) patients who had diabetes mellitus, 46 (78.0%) had hypertension, 17 (28.8%) were obese, nine (15.3%) had a family history of MI, and three (5.1%) were smokers. There were 43 (72.9%) patients who were illiterate. In our study, eight (13.6%) females were found to have sinus bradycardia, seven (11.9%) had sinus tachycardia, three (5.1%) had atrial fibrillation, and 24 (40.7%) had complete heart block. Mortality was noted in five (8.5%) patients. Conclusions Women with an acute inferior wall MI had a higher rate of complete heart block and adverse in-hospital outcomes. Female gender itself with inferior wall MI may be at risk for in-hospital adverse outcomes.

Feature selection and risk prediction for patients with coronary artery disease using data mining

Coronary artery disease (CAD) is an important cause of mortality across the globe. Early risk prediction of CAD would be able to reduce the death rate by allowing early and targeted treatments. In healthcare, some studies applied data mining techniques and machine learning algorithms on the risk prediction of CAD using patient data collected by hospitals and medical centers. However, most of these studies used all the attributes in the datasets which might reduce the performance of prediction models due to data redundancy. The objective of this research is to identify significant features to build models for predicting the risk level of patients with CAD. In this research, significant features were selected using three methods (i.e., Chi-squared test, recursive feature elimination, and Embedded Decision Tree). Synthetic Minority Over-sampling Technique (SMOTE) oversampling technique was implemented to address the imbalanced dataset issue. The prediction models were built based on the identified significant features and eight machine learning algorithms, utilizing Acute Coronary Syndrome (ACS) datasets provided by National Cardiovascular Disease Database (NCVD) Malaysia. The prediction models were evaluated and compared using six performance evaluation metrics, and the top-performing models have achieved AUC more than 90%. Graphical abstract.